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http://genome-www.stanford.edu/Saccharomyces 

Functional organization of the yeast proteome by systematic analysis of protein complexes ANNE-CLAUDE GAVIN, at al  Nature 415, 141–147 (10 January 2002)

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry YUEN HO, et al  Nature 415, 180–183 (10 January 2002)

 

2001 was the year of the  genome. 2002 opens with new insights into the proteome, an organism's total quota of proteins. For  the first time, the countless  ways proteins group together  in a yeast cell have been systematically logged.  Although the classification is currently incomplete, unravelling protein collaborations could change  the way drugs are  discovered or designed.  Proteomics - logging proteins and working out what they do - is a colossal task. Dissecting the protein clusters that carry out many tasks in the cell is the start. Superti-Furga's team (EMBL) and that of Mike Tyers of the Mount Sinai Hospital in Toronto, Canada have waded into the 6,000 member proteome of the  budding yeast. Each group captured a quarter of them and identified their interacting partners. around 85% of the proteins associate with others. One   promiscuous protein has at least 96 associates. Many turn up in more than one complex or pathway: both groups are now  building up dense networks representing the complexes and their shared  components. Protein interactions on a proteome-wide scale have already been analysed in several ways. In a pair of landmark papers, Uetz et al.(2000) and Ito et al. (2001) adapted the yeast 'two-hybrid' assay - a means of assessing whether two single proteins interact - into a high-throughput method of mapping pair-wise protein interactions on a large scale. The authors collectively identified over 4,000 protein–protein interactions in S. cerevisiae. Another group has developed a microarray technology in which purified, active proteins from almost the entire yeast proteome are printed onto a microscope slide at high density, such that thousands of protein interactions (and other protein functions) can be assayed simultaneously. Large-scale efforts to characterize protein complexes are generally rate-limited by the need for a nearly pure preparation of each complex. In the new studies by Gavin (2002) protein complexes were purified by attaching tags to hundreds of different proteins (to create 'bait' proteins). They then introduced DNA encoding these bait proteins into yeast cells, allowing the modified proteins to be expressed in the cells and to form physiological complexes with other proteins. Then, using the tag, each bait protein was pulled out, often fishing out the entire complex with it (hence the term 'bait'). The proteins extracted with the tagged bait were identified using standard mass-spectrometry methods (Ho, 2002). Applying this approach on a proteome-wide scale, Gavin et al. have identified 1,440 distinct proteins within 232 multiprotein complexes in yeast. Furthermore, they found that most of these complexes have a component in common with at least one other multiprotein assembly, suggesting a means of coordinating cellular functions into a higher-order network of interacting protein complexes. An understanding of this high-order organization will undoubtedly offer insight into corresponding networks in other organisms, as most yeast complexes have counterparts in more complex species.

 With an estimated 80,000 –100,000 unique compounds produced in the plant kingdom, elucidating these metabolic networks is likely to be an exciting endeavor. I have taken just a few examples from both the macro and micro level to illustrate the concepts in the field of plant metabolic engineering and to discuss the potential and the limitations of current approaches and future potential in this exciting field.  More extensive coverage will be provided in later chapters.

 

Faced with the avalanche of genomic sequences and data on messenger RNA expression, biological scientists are confronting a frightening prospect: piles of information but only flakes of knowledge. How can the thousands of sequences being determined and deposited, and the thousands of expression profiles being generated by the new array methods, be synthesized into useful knowledge? What form will this knowledge take? These are questions being addressed by scientists in the field known as 'functional genomics'.

 

David Eisenberg, 2000

 

Two functional protein networks. a, Network of protein interactions and predicted functional links involving silencing information regulator (SIR) proteins. Filled circles represent proteins of known function; open circles represent proteins of unknown function, represented only by their Saccharomyces genome sequence numbers (http://genome-www.stanford.edu/Saccharomyces). Solid lines show experimentally determined interactions, as summarized in the Database of Interacting Proteins(http://dip.doe-mbi.ucla.edu). Dashed lines show functional links predicted by the Rosetta Stone method. Dotted lines show functional links predicted by phylogenetic profiles. Some predicted links are omitted for clarity. b, Network of predicted functional linkages involving the yeast prion protein Sup35. The dashed line shows the only experimentally determined interaction. The other functional links were calculated from genome and expression data by a combination of methods, including phylogenetic profiles, Rosetta stone linkages and mRNA expression. Linkages predicted by more than one method, and hence particularly reliable, are shown by heavy lines.